A Comparison of an Adaptive Neuro-Fuzzy and Frequency Ratio Model to Landslide-Susceptibility Mapping along Forest Road Networks
Bibliographic record
Abstract
In this research, we used the integration of frequency ratio and adaptive neuro-fuzzy modeling (ANFIS) to predict landslide susceptibility along forest road networks in the Hyrcanian Forest, northern Iran. We began our study by first mapping landslide locations during an extensive field survey. In addition, we then selected landslide-conditioning factors, such as slope, aspect, altitude, rainfall, geology, soil, road age, and slip position from the available Geographic Information System (GIS) data. Following this, we developed Adaptive Neuro-Fuzzy Inference System (ANFIS) models with two different membership functions (MFs) in order to generate landslide susceptibility maps. We applied a frequency ratio model to the landslide susceptibility mapping and compared the results with the probabilistic ANFIS model. Finally, we calculated map accuracy by evaluating receiver-operating characteristics (ROC). The validation results yielded 70.7% accuracy using the triangular MF model, 67.8% accuracy using the Gaussian MF model, and 68.8% accuracy using the frequency ratio model. Our results indicated that the ANFIS is an effective tool for regional landslide susceptibility assessment, and the maps produced in the study area can be used for natural hazard management in the landslide-prone area of the Hyrcanian region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".